--- name: brand-listening description: > Social listening and brand reputation research using Bright Data's web scraping infrastructure. Collects what real people are saying about a brand, product, or person across Reddit, X/Twitter, Instagram, TikTok, YouTube, news, and review sites — then classifies sentiment, clusters themes, and delivers a cited digest with actionable recommendations. Use this skill when the user wants to know what people are saying about their brand, monitor social media mentions, gauge public sentiment, track online reputation, find complaints or advocacy, measure buzz around a launch, or do social listening / brand monitoring / sentiment analysis. Also use when the user mentions brand mentions, brand health, reputation tracking, or "what's the internet saying about us". --- # Brand Listening Find out what people are *actually* saying about a brand across social platforms, news, and reviews — powered by live web data, not stale training knowledge. Combines the Bright Data CLI (`bdata`) for collection with a sentiment + theme analysis layer to deliver a cited, actionable digest. **Never answer brand-sentiment questions from training knowledge alone.** Public sentiment changes daily. Always collect live mentions first, then classify and synthesize. ## Prerequisites 1. Bright Data CLI installed: ```bash curl -fsSL https://cli.brightdata.com/install.sh | bash ``` 2. One-time login completed: ```bash bdata login # or: bdata login --device (SSH / headless) ``` Verify before collecting: ```bash if ! command -v bdata >/dev/null 2>&1; then echo "bdata CLI not installed — see skills/bright-data-best-practices/references/cli-setup.md" elif ! bdata zones >/dev/null 2>&1; then echo "bdata not authenticated — run: bdata login" fi ``` Halt and route to setup if either check fails. ## Core Workflow 1. **Clarify scope** — Which brand/product/person? Which platforms? What time window (default: last 30 days)? What does the user want to *do* with it (general health check, launch monitoring, complaint triage, advocacy hunting)? 2. **Discover, then collect** — Use `bdata search` to find where the brand is being discussed, then `bdata pipelines` to pull structured mentions from each platform. Parallelize independent calls. 3. **Normalize** — Collapse every raw result into the single mention schema in [references/sentiment-and-output.md](references/sentiment-and-output.md) before any analysis. 4. **Classify & cluster** — Assign sentiment per mention (with a reason), then group mentions into themes. Follow the sentiment guardrails — never inflate either side. 5. **Deliver** — Produce the cited digest (Output A), optionally with the structured dataset (Output B). Every report ends with a "So what — recommendations" section. ## Data Collection Rules - **Discovery first.** You rarely have the right URLs up front. Run `bdata search " site:reddit.com" --json` (and per-platform variants) to find threads, profiles, and articles, *then* feed those URLs to pipelines. - **Prefer `bdata pipelines`** over `bdata scrape` whenever a pipeline exists for the platform — pipelines return clean structured JSON (author, date, engagement, text). - **Always pass `--json`** when you need to parse or pipe output. - **Be cost-efficient** — a standard sweep is ~6–12 `bdata` calls, not 50. Pull the highest-signal threads/profiles, not everything. - **Parallelize** independent calls across multiple Bash tool calls in one response. - **Every mention needs a source URL.** No unattributed quotes, ever. - **Never fabricate sentiment or fill gaps.** If a platform returns nothing, report it in "Gaps & caveats". ## Platform Modules Pick the platforms that fit the brand. Consumer/cultural brands skew TikTok/Instagram/Reddit; B2B/SaaS skews Reddit/X/review sites; local businesses skew Google Maps reviews. ### Reddit — honest, unfiltered sentiment ```bash # Discover relevant threads bdata search " site:reddit.com" --json bdata search " review reddit" --json # Pull structured post + comment data from the threads found bdata pipelines reddit_posts "" --json -o reddit.json ``` Reddit is the single best source for candid opinions brand channels hide. Prioritize it. ### X / Twitter — real-time reaction ```bash bdata search "" --json # find recent discussion bdata pipelines x_posts "" --json -o x.json ``` ### Instagram — brand aesthetics, comments, advocacy ```bash bdata pipelines instagram_posts "https://www.instagram.com//" --json -o ig_posts.json bdata pipelines instagram_comments "" --json -o ig_comments.json ``` ### TikTok — cultural relevance, viral sentiment ```bash bdata pipelines tiktok_posts "https://www.tiktok.com/@" --json -o tt_posts.json bdata pipelines tiktok_comments "" --json -o tt_comments.json ``` ### YouTube — reviews, tutorials, long-form opinion (comments are gold) ```bash bdata search " review youtube" --json bdata pipelines youtube_videos "" --json -o yt_videos.json bdata pipelines youtube_comments "" 100 --json -o yt_comments.json # url + num_comments ``` ### Reviews — structured customer sentiment ```bash # App-based products bdata pipelines google_play_store "" --json -o play.json bdata pipelines apple_app_store "" --json -o appstore.json # Local / physical businesses bdata pipelines google_maps_reviews "" 90 --json -o gmaps.json # url + days_limit # Facebook page reviews bdata pipelines facebook_company_reviews "" 50 --json -o fb_reviews.json # url + num # SaaS / software — discover then scrape (no pipeline) bdata search " site:g2.com" --json bdata search " site:capterra.com" --json bdata scrape "" ``` ### News & press — coverage and tone ```bash bdata search "" --json # general SERP, scan for news bdata scrape "" # pull full article text for tone ``` > **Pipeline names change.** Always confirm with `bdata pipelines list` before hardcoding a type. Names are inconsistent across platforms (`tiktok_posts` plural, `reddit_posts` plural, `x_posts`). The `data-feeds` skill has the verified list. ## Choosing What to Collect | User says... | Collect from | |---|---| | "What are people saying about us / my brand" | Reddit + X + reviews + news (broad sweep) | | "How did our launch land" / "buzz around X" | TikTok + Instagram + X + YouTube (recency-focused) | | "Find complaints / what people hate" | Reddit + reviews (G2/Capterra/app stores) + YouTube comments | | "Who's advocating for us / fans" | Instagram + TikTok + X (high-engagement positive posts) | | "Reputation / sentiment over time" | Same sources, two windows — compare prior vs current | | Local business reputation | Google Maps reviews + Facebook reviews | ## Sentiment, Themes & Output Read [references/sentiment-and-output.md](references/sentiment-and-output.md) for: - The **normalized mention record** schema (use it as the row shape for both analysis and the dataset output). - The **sentiment method + guardrails** (classify from text as written; sarcasm/ambiguous → `mixed`; always report counts *and* percentages with the denominator). - **Theme clustering** — where the actionable insight lives. - **Output templates** — A (digest report), B (structured dataset), C (both). ## Output Quality Standards 1. **Every mention has a source URL** — no unattributed claims. 2. **Facts separate from interpretation** — the quote is the fact; your sentiment label is interpretation. Keep them distinct. 3. **Percentages always carry N** — "61% positive of 142 classified mentions", never a bare percentage. 4. **Be honest about gaps** — list platforms that returned nothing this window. Note small samples that can't generalize. 5. **Date-stamp everything** — "Data collected on · window: last days". 6. **End with "So what"** — every report closes with 2–4 actionable recommendations, each grounded in a theme from the data. Raw mentions without interpretation are not a deliverable.